Files
agent/papers/items/2026-2605-26720-towards-feedback-to-plan-decisions-for-self-evolving-llm-agents-in-cuda-kernel-g.md
2026-07-08 12:25:30 +08:00

62 lines
1.4 KiB
Markdown

# Paper: Towards Feedback-to-Plan Decisions for Self-Evolving LLM Agents in CUDA Kernel Generation
---
type: paper
title: Towards Feedback-to-Plan Decisions for Self-Evolving LLM Agents in CUDA Kernel Generation
authors: Yee Hin Chong, Jiaming Wu, Youhui Zhang, Peng Qu
year: 2026
venue: arXiv
url: https://arxiv.org/abs/2605.26720
code_url:
source: arxiv
collected_at: 2026-07-08
published_at: 2026-05-26
updated_at: 2026-05-26
status: queued
relevance: high
topics:
- agent-evaluation
- planning
- reasoning
- tool-use
methods:
-
benchmarks:
-
models:
-
datasets:
- cs.AI
related_concepts:
-
related_jobs:
-
related_experiments:
-
related_projects:
-
collection_score: 15
collection_queries: planning-agent
---
## One-line Takeaway
Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.
## Why Collected
- matched queries: planning-agent
- inferred topics: agent-evaluation, planning, reasoning, tool-use
- arXiv categories: cs.AI
- collection score: 15
## Review Checklist
- Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
- Does it include a benchmark, dataset, code, or reproducible experimental setup?
- Should it be promoted from `queued` to `skimmed` or `summarized`?
## Links
- arXiv: https://arxiv.org/abs/2605.26720